This invention relates generally to power generating systems and, more particularly, to a method and system for emission control and combustion optimization in fossil fuel fired boilers with an array of fiber grating-based sensing modules.
In numerous industrial environments, a hydrocarbon fuel is burned in stationary combustors (e.g., boilers or furnaces) to produce heat to raise the temperature of a fluid, e.g., water. For example, the water is heated to generate steam, and this steam is then used to drive turbine generators that output electrical power. Such industrial combustors typically employ an array of many individual burner elements to combust the fuel. In addition, various means of combustion control, such as overfire air, staging air, reburning systems and selective non-catalytic reduction systems, can be employed to enhance combustion conditions and reduce emissions of oxides of nitrogen (NOx) and carbon monoxide.
Emissions and efficiency are key performance metrics for industrial boilers that are often used for generation of process steam required for industries. Emissions and efficiency are important performance metrics for utility boilers, which are mainly used for power generation along with generation of process steam. Poor or non-uniform combustion leads to low availability, low peak steam/power generation, low efficiency and high emissions. Conventional industrial boiler and utility boiler controls are often based on data driven or empirical models with limited feedback from the boiler environment due to limited real-time, multi-point monitoring and sensing capabilities. Most sensing systems that are used to monitor NOx, CO and temperature use single-point sensors that are typically placed in the boilers exhaust area. Often, gas sensing is ex-situ and extractive in nature.
For a combustion system, such as a multiple burner boiler furnace or a gas turbine combustor, to operate efficiently and to produce an acceptably complete combustion that generates byproducts falling within the limits imposed by environmental regulations and design constraints, all individual burners in the combustion system must operate cleanly and efficiently and all combustion modification systems must be properly balanced and adjusted. Emissions of NOx, carbon monoxide (CO), mercury (Hg) and/or other byproducts generally are monitored to ensure compliance with environmental regulations and acceptable system operation. Such operating conditions and/or gas emissions can be monitored using sensors.
Due to non-uniform combustion, power generation oriented utility boilers or process steam generating industrial boilers tend to operate at lower efficiencies than the design limits, thus resulting in high operating and maintenance costs. In addition, limited sensing and actuation capabilities and limited real-time information regarding boiler condition leads to solutions that are not very effective for reducing emissions or improving efficiency. Many conventional industrial or utility boilers suffer in this context and provide only limited improvements in emission reduction and/or efficiency.
Conventional electric-based gas sensors operate at temperatures less than about 500° C. due to sensing material and/or device limitations. The reliability of conventional electric-based gas sensors has suffered from several problems. These gas sensors fail to operate when the environmental temperature is higher than the sensor's operating temperature. It is also difficult to predict the gas concentration due to the temperature-dependent nonlinear sensitivity characteristics. Additionally, electric-based gas sensors suffer from long-term stability or sensitivity degradation due to thermal effects on the electrical interfaces to supply power or transmitting signal. Further, they are not suitable for high-voltage and explosive environments. Finally, electric-based sensors are not suitable for multiple point gas sensing applications.
Solid-state semiconductor gas sensing technology generally performs better than the electrochemical gas sensing technology due to the use of a wide band-gap material that allows high temperature operation up to 500° C. Despite the drift due to the temperature-dependent resistivity at higher temperature, solid-state semiconductor gas sensors provide an acceptable performance as a point sensor. However, these devices also tend to fail at higher temperatures due to thermal effects on the electrical interfaces to supply power or a transmitting signal as well. Further, because the sensing performance varies significantly with environmental temperature, pressure variations and/or toxic gas variations, solid-state semiconductor gas sensors require a constant calibration to maintain accuracy.
There is no systematic method to adjust the air and fuel flows for reducing spatial variance of emissions at a boiler's exit to reduce stack emissions. Rather, conventional boiler combustion optimization procedures are primarily established using the boiler expert's domain knowledge. Data-driven models such as Neural Networks and Expert Systems lack the rigor and fidelity and thereby have limited impact on efficiency because these models are dependent on data quality and are prone to data noise and inaccuracies. Model-based optimization systems that incorporate the physics of the combustion system along with accurate and spatially dense data provided by a fiberoptic sensor array overcome the limitations of currently available boiler optimization products that rely on data limited in terms of availability, accuracy and spatial density due to the harsh environment of boiler systems and sensor capability limitations.